v0.26.0

Added

• path_filter parameter in pw.io.s3.read and pw.io.minio.read functions. It enables post-filtering of object paths using a wildcard pattern (*, ?), allowing exclusion of paths that pass the main path filter but do not match path_filter.
• Input connectors now support backpressure control via max_backlog_size, allowing to limit the number of read events in processing per connector. This is useful when the data source emits a large initial burst followed by smaller, incremental updates.
• pw.reducers.count_distinct and pw.reducers.count_distinct_approximate to count the number of distinct elements in a table. The pw.reducers.count_distinct_approximate allows you to save memory by decreasing the accuracy. It is possible to control this tradeoff by using the precision parameter.
• pw.Table.join (and its variants) now has two additional parameters - left_exactly_once and right_exactly_once. If the elements from a side of a join should be joined exactly once, *_exactly_once parameter of the side can be set to True. Then after getting a match an entry will be removed from the join state and the memory consumption will be reduced.

Changed

• Delta table compression logging has been improved: logs now include table names, and verbose messages have been streamlined while preserving details of important processing steps.
• Improved initialization speed of pw.io.s3.read and pw.io.minio.read.
• pw.io.s3.read and pw.io.minio.read now limit the number and the total size of objects to be predownloaded.
• BREAKING optimized the implementation of pw.reducers.min, pw.reducers.max, pw.reducers.argmin, pw.reducers.argmax, pw.reducers.any reducers for append-only tables. It is a breaking change for programs using operator persistence. The persisted state will have to be recomputed.
• BREAKING optimized the implementation of pw.reducers.sum reducer on float and np.ndarray columns. It is a breaking change for programs using operator persistence. The persisted state will have to be recomputed.
• BREAKING the implementation of data persistence has been optimized for the case of many small objects in filesystem and S3 connectors. It is a breaking change for programs using data persistence. The persisted state will have to be recomputed.
• BREAKING the data snapshot logic in persistence has been optimized for the case of big input snapshots. It is a breaking change for programs using data persistence. The persisted state will have to be recomputed.
• Improved precision of pw.reducers.sum on float columns by introducing Neumeier summation.